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modelling. MISSION You will actively contribute to the development and evaluation of new hybrid computational method to predict biological tissue deformation with subject-specific material properties
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). The institute also maintains locations in Dedelow and Paulinenaue. The position will be based in the Research Platform “Data Analysis and Simulation „within the Working Group “Ecosystem Modelling“ under
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Modeling, Analysis & Prediction of Particle-laden Real-Gas Supersonic Turbulence. (Ref. 10267290001)
Description Mission: Carry out the modeling, analysis and prediction of real gas supersonic turbulence. Fuctions to be developed: Develop tools to aid analysis. Perform experimental and computational analyses
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position in quantitative developmental and family systems methods. Competitive candidates will have expertise in measurement, latent variable modeling, or novel data integration approaches. Particular
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for the Advancement of Surgery) initiative. The Research Associate will be responsible for developing machine learning algorithms and creating predictive models. The ideal candidate must demonstrate a robust background
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applicant will gain considerable research skills in multiscale materials modeling. Moreover, soft skills in programming, problem solving, analytical thinking, teamwork, and international collaborations will
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, advanced high-parameter flow-cytometry, as well as murine models and human organoid technology to investigate mechanisms of longevity of immunological T and B cell memory. A strong interest in quantitative
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-parameter flow-cytometry, as well as murine models and human organoid technology to investigate mechanisms of longevity of immunological T and B cell memory. A strong interest in quantitative disciplines
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dysfunction in heart failure, sepsis, and related inflammatory conditions. Utilize in vitro and in vivo models, including mouse models of sepsis (e.g., CLP, LPS), ischemiareperfusion injury, and vascular injury
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. The successful candidate should apply state-of-the-art methods in either aquatic ecology and biodiversity research (e.g., environmental omics, eDNA, etc.) or hydrology (e.g. integrated modeling and/or AI-based